Diabetes Prediction Web App

A Diabetes Prediction web app using machine learning is an application that utilizes a trained machine learning model to predict whether a person has diabetes based on input features such as glucose levels, blood pressure, BMI, age, etc

Prerequisites

• Python
• Pip
• Git (optional)

Getting Started

1. Clone the Repository:

   git clone https://github.com/shubham5027/diabetes-prediction-webapp.git
   cd diabetes-prediction-webapp

2. Install Dependencies:

   pip install -r requirements.txt

3. Run the App:

   python -m streamlit run app.py

4. Access the Web App:

https://diabetes-prediction-webapp-using-machine-learning-fxgmyqg5kzjz.streamlit.app/

Usage

• Use the sliders in the sidebar to input values for features like pregnancies, glucose, blood pressure, etc.
• Click the "Predict" button to see the model's prediction.
• The result will be displayed, indicating whether the person is predicted to have diabetes or not.

Model Information

The machine learning model used in this app is a logistic regression model trained on the Diabetes dataset.

Additional Notes

• The web app is built using Streamlit, a Python library for creating web applications with minimal effort.
• The trained machine learning model is saved in a file named your_model.pkl. Replace it with your actual trained model file.

License

This project is licensed under the MIT License.